Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVIReportar como inadecuado




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State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China





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Academic Editors: Naser El-Sheimy, Zahra Lari, Adel Moussa, Josef Kellndorfer, Richard Müller and Prasad S. Thenkabail

Abstract Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index NDVI datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model NDVI-BSFM for accurately and effectively building frequent high spatial resolution Landsat-like NDVI datasets by integrating Moderate Resolution Imaging Spectroradiometer MODIS and Landsat NDVI. Experimental comparisons with the results obtained using other popular methods i.e., the Spatial and Temporal Adaptive Reflectance Fusion Model STARFM, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model ESTARFM, and the Flexible Spatiotemporal DAta Fusion FSDAF method showed that our proposed method has the following advantages: 1 it can obtain more accurate estimates; 2 it can retain more spatial detail; 3 its prediction accuracy is less dependent on the quality of the MODIS NDVI on the specific prediction date; and 4 it produces smoother NDVI time series profiles. All of these advantages demonstrate the strengths and the robustness of the proposed NDVI-BSFM in providing reliable high spatial and temporal resolution NDVI datasets to support other land surface process studies. View Full-Text

Keywords: Bayesian; data fusion; Landsat; MODIS; NDVI Bayesian; data fusion; Landsat; MODIS; NDVI





Autor: Limin Liao, Jinling Song * , Jindi Wang, Zhiqiang Xiao and Jian Wang

Fuente: http://mdpi.com/



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